{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4020a4d1-a514-4c4b-99a3-499f0842efaf"
    }
   },
   "source": [
    "# GSEAPY Example\n",
    "Examples to use ``GSEApy`` inside python console"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib inline\n",
    "# %config InlineBackend.figure_format='retina' # mac\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import pandas as pd\n",
    "import gseapy as gp\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d2c0662b-fd5a-4240-8c31-45e8489623df"
    }
   },
   "source": [
    "**Check gseapy version**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.1.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "26c4261b-7326-47d9-b353-c6a4bd1b04c5"
    }
   },
   "source": [
    "## Biomart API  \n",
    "\n",
    "Don't use this if you don't know Biomart\n",
    "\n",
    "Warning: This API has limited support now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert gene identifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gseapy import Biomart \n",
    "bm = Biomart()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ensembl_gene_id</th>\n",
       "      <th>external_gene_name</th>\n",
       "      <th>entrezgene_id</th>\n",
       "      <th>go_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>ENSG00000182968</td>\n",
       "      <td>SOX1</td>\n",
       "      <td>6656</td>\n",
       "      <td>GO:0021884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>ENSG00000182968</td>\n",
       "      <td>SOX1</td>\n",
       "      <td>6656</td>\n",
       "      <td>GO:0030900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>ENSG00000182968</td>\n",
       "      <td>SOX1</td>\n",
       "      <td>6656</td>\n",
       "      <td>GO:0048713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>ENSG00000182968</td>\n",
       "      <td>SOX1</td>\n",
       "      <td>6656</td>\n",
       "      <td>GO:1904936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ENSG00000182968</td>\n",
       "      <td>SOX1</td>\n",
       "      <td>6656</td>\n",
       "      <td>GO:1990830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ensembl_gene_id external_gene_name  entrezgene_id       go_id\n",
       "36  ENSG00000182968               SOX1           6656  GO:0021884\n",
       "37  ENSG00000182968               SOX1           6656  GO:0030900\n",
       "38  ENSG00000182968               SOX1           6656  GO:0048713\n",
       "39  ENSG00000182968               SOX1           6656  GO:1904936\n",
       "40  ENSG00000182968               SOX1           6656  GO:1990830"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## view validated marts\n",
    "# marts = bm.get_marts()\n",
    "## view validated dataset\n",
    "# datasets = bm.get_datasets(mart='ENSEMBL_MART_ENSEMBL')\n",
    "## view validated attributes\n",
    "# attrs = bm.get_attributes(dataset='hsapiens_gene_ensembl') \n",
    "## view validated filters\n",
    "# filters = bm.get_filters(dataset='hsapiens_gene_ensembl')\n",
    "## query results\n",
    "queries ={'ensembl_gene_id': ['ENSG00000125285','ENSG00000182968'] } # need to be a dict object\n",
    "results = bm.query(dataset='hsapiens_gene_ensembl', \n",
    "                   attributes=['ensembl_gene_id', 'external_gene_name', 'entrezgene_id', 'go_id'],\n",
    "                   filters=queries)\n",
    "results.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ensembl_gene_id       object\n",
       "external_gene_name    object\n",
       "entrezgene_id          Int32\n",
       "go_id                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mouse gene symbols maps to Human, or Vice Versa\n",
    "\n",
    "This is useful when you have troubles to convert gene symbols between human and mouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gseapy import Biomart \n",
    "bm = Biomart()\n",
    "# note the dataset and attribute names are different\n",
    "m2h = bm.query(dataset='mmusculus_gene_ensembl',\n",
    "               attributes=['ensembl_gene_id','external_gene_name',\n",
    "                           'hsapiens_homolog_ensembl_gene',\n",
    "                           'hsapiens_homolog_associated_gene_name'])\n",
    "\n",
    "h2m = bm.query(dataset='hsapiens_gene_ensembl',\n",
    "               attributes=['ensembl_gene_id','external_gene_name',\n",
    "                           'mmusculus_homolog_ensembl_gene',\n",
    "                           'mmusculus_homolog_associated_gene_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# h2m.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gene Symbols Conversion for the GMT file\n",
    "This is useful when runing GSEA for non-human species\n",
    "\n",
    "**e.g. Convert Human gene symbols to Mouse.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EGF', 'IL1R1', 'IL1R2', 'HSPA1L', 'CACNA2D2', 'CACNA2D1', 'CACNA2D4', 'CACNA2D3', 'MAPK8IP3', 'MAPK8IP1']\n"
     ]
    }
   ],
   "source": [
    "# get a dict symbol mappings\n",
    "h2m_dict = {}\n",
    "for i, row in h2m.loc[:,[\"external_gene_name\", \"mmusculus_homolog_associated_gene_name\"]].iterrows():\n",
    "    if row.isna().any(): continue\n",
    "    h2m_dict[row['external_gene_name']] = row[\"mmusculus_homolog_associated_gene_name\"]\n",
    "# read gmt file into dict\n",
    "kegg = gp.read_gmt(path=\"tests/extdata/enrichr.KEGG_2016.gmt\")\n",
    "print(kegg['MAPK signaling pathway Homo sapiens hsa04010'][:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Egf', 'Il1r1', 'Il1r2', 'Hspa1l', 'Cacna2d2', 'Cacna2d1', 'Cacna2d4', 'Cacna2d3', 'Mapk8ip3', 'Mapk8ip1']\n"
     ]
    }
   ],
   "source": [
    "kegg_mouse = {}\n",
    "for term, genes in kegg.items():\n",
    "    new_genes = []\n",
    "    for gene in genes:\n",
    "        if gene in h2m_dict:\n",
    "            new_genes.append(h2m_dict[gene])\n",
    "    kegg_mouse[term] = new_genes\n",
    "print(kegg_mouse['MAPK signaling pathway Homo sapiens hsa04010'][:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Msigdb API\n",
    "\n",
    "Down load `gmt` file from: https://data.broadinstitute.org/gsea-msigdb/msigdb/release/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gseapy import Msigdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "msig = Msigdb()\n",
    "# mouse hallmark gene sets\n",
    "gmt = msig.get_gmt(category='mh.all', dbver=\"2023.1.Mm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "two helper method \n",
    "```python\n",
    "# list msigdb version you wanna query\n",
    "msig.list_dbver()\n",
    "# list categories given dbver.  \n",
    "msig.list_category(dbver=\"2023.1.Hs\") # mouse\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ctnnb1', 'Jag1', 'Myc', 'Notch1', 'Ptch1', 'Trp53', 'Axin1', 'Ncstn', 'Rbpj', 'Psen2', 'Wnt1', 'Axin2', 'Hey2', 'Fzd1', 'Frat1', 'Csnk1e', 'Dvl2', 'Hey1', 'Gnai1', 'Lef1', 'Notch4', 'Ppard', 'Adam17', 'Tcf7', 'Numb', 'Ccnd2', 'Ncor2', 'Kat2a', 'Nkd1', 'Hdac2', 'Dkk1', 'Wnt5b', 'Wnt6', 'Dll1', 'Skp2', 'Hdac5', 'Fzd8', 'Dkk4', 'Cul1', 'Jag2', 'Hdac11', 'Maml1']\n"
     ]
    }
   ],
   "source": [
    "print(gmt['HALLMARK_WNT_BETA_CATENIN_SIGNALING'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "049b8015-83fe-4037-ad66-76c2e3989f96"
    }
   },
   "source": [
    "## Enrichr API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "b8abb0c6-1da7-4df1-b945-3cb07e960211"
    }
   },
   "source": [
    "**See all supported enrichr library names**  \n",
    "\n",
    "Select database from **{ 'Human', 'Mouse', 'Yeast', 'Fly', 'Fish', 'Worm' }**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ARCHS4_Cell-lines',\n",
       " 'ARCHS4_IDG_Coexp',\n",
       " 'ARCHS4_Kinases_Coexp',\n",
       " 'ARCHS4_TFs_Coexp',\n",
       " 'ARCHS4_Tissues',\n",
       " 'Achilles_fitness_decrease',\n",
       " 'Achilles_fitness_increase',\n",
       " 'Aging_Perturbations_from_GEO_down',\n",
       " 'Aging_Perturbations_from_GEO_up',\n",
       " 'Allen_Brain_Atlas_10x_scRNA_2021']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# default: Human\n",
    "names = gp.get_library_name()\n",
    "names[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cellular_Component_AutoRIF',\n",
       " 'Cellular_Component_AutoRIF_Predicted_zscore',\n",
       " 'GO_Biological_Process_2018',\n",
       " 'GO_Biological_Process_AutoRIF',\n",
       " 'GO_Biological_Process_AutoRIF_Predicted_zscore',\n",
       " 'GO_Cellular_Component_2018',\n",
       " 'GO_Cellular_Component_AutoRIF',\n",
       " 'GO_Cellular_Component_AutoRIF_Predicted_zscore',\n",
       " 'GO_Molecular_Function_2018',\n",
       " 'GO_Molecular_Function_AutoRIF']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# yeast\n",
    "yeast = gp.get_library_name(organism='Yeast') \n",
    "yeast[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Parse Enrichr library into dict**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MLH1', 'ECM10', 'RLI1', 'SSB1', 'SSB2', 'YTA12', 'MSH2', 'CDC6', 'HMI1', 'YNL247W', 'MSH6', 'SSQ1', 'MCM7', 'SRS2', 'HSP104', 'SSA1', 'MCX1', 'SSC1', 'ARP2', 'ARP3', 'SSE1', 'SMC2', 'SSZ1', 'TDA10', 'ORC5', 'VPS4', 'RBK1', 'SSA4', 'NEW1', 'ORC1', 'SSA2', 'KAR2', 'SSA3', 'DYN1', 'PGK1', 'VPS33', 'LHS1', 'CDC123', 'PMS1']\n"
     ]
    }
   ],
   "source": [
    "## download library or read a .gmt file\n",
    "go_mf = gp.get_library(name='GO_Molecular_Function_2018', organism='Yeast')\n",
    "print(go_mf['ATP binding (GO:0005524)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Over-representation analysis by Enrichr web services\n",
    "\n",
    "The only requirement of input is a list of gene symbols.\n",
    "\n",
    "For online web services, gene symbols are not case sensitive.\n",
    "\n",
    "\n",
    "- `gene_list` accepts\n",
    "    - ``pd.Series``\n",
    "    -  ``pd.DataFrame`` \n",
    "    -  ``list`` object\n",
    "    - ``txt`` file (one gene symbol per row)\n",
    "\n",
    "\n",
    "- `gene_sets` accepts:\n",
    "\n",
    "    Multi-libraries names supported, separate each name by comma or input a list. \n",
    "\n",
    "For example:\n",
    "```python\n",
    "    # gene_list\n",
    "    gene_list=\"./data/gene_list.txt\", \n",
    "    gene_list=glist\n",
    "    # gene_sets\n",
    "    gene_sets='KEGG_2016'  \n",
    "    gene_sets='KEGG_2016,KEGG_2013'\n",
    "    gene_sets=['KEGG_2016','KEGG_2013']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IGKV4-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CD55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IGKC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PPFIBP1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABHD4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "0          IGKV4-1\n",
       "1             CD55\n",
       "2             IGKC\n",
       "3          PPFIBP1\n",
       "4            ABHD4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in an example gene list\n",
    "gene_list = pd.read_csv(\"./tests/data/gene_list.txt\",header=None, sep=\"\\t\")\n",
    "gene_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['IGKV4-1', 'CD55', 'IGKC', 'PPFIBP1', 'ABHD4', 'PCSK6', 'PGD', 'ARHGDIB', 'ITGB2', 'CARD6']\n"
     ]
    }
   ],
   "source": [
    "# convert dataframe or series to list\n",
    "glist = gene_list.squeeze().str.strip().to_list()\n",
    "print(glist[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Over-representation analysis via Enrichr web services\n",
    "\n",
    "This is an Example of the Enrichr analysis\n",
    "\n",
    "**NOTE**: \n",
    "1. Enrichr Web Sevices need `gene symbols` as input\n",
    "2. `Gene symbols` will convert to upcases automatically.\n",
    "3. (Optional) Input an user defined `background gene list`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Enrichr Web Serives (without a backgound input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if you are only intrested in dataframe that enrichr returned, please set outdir=None\n",
    "enr = gp.enrichr(gene_list=gene_list, # or \"./tests/data/gene_list.txt\", \n",
    "                 gene_sets=['MSigDB_Hallmark_2020','KEGG_2021_Human'], \n",
    "                 organism='human', # don't forget to set organism to the one you desired! e.g. Yeast\n",
    "                 outdir=None, # don't write to disk \n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene_set</th>\n",
       "      <th>Term</th>\n",
       "      <th>Overlap</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Adjusted P-value</th>\n",
       "      <th>Old P-value</th>\n",
       "      <th>Old Adjusted P-value</th>\n",
       "      <th>Odds Ratio</th>\n",
       "      <th>Combined Score</th>\n",
       "      <th>Genes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MSigDB_Hallmark_2020</td>\n",
       "      <td>IL-6/JAK/STAT3 Signaling</td>\n",
       "      <td>19/87</td>\n",
       "      <td>1.197225e-09</td>\n",
       "      <td>5.986123e-08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.844694</td>\n",
       "      <td>140.612324</td>\n",
       "      <td>IL4R;TGFB1;IL1R1;IFNGR1;IL10RB;ITGB3;IFNGR2;IL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSigDB_Hallmark_2020</td>\n",
       "      <td>TNF-alpha Signaling via NF-kB</td>\n",
       "      <td>27/200</td>\n",
       "      <td>3.220898e-08</td>\n",
       "      <td>5.368163e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.841568</td>\n",
       "      <td>66.270963</td>\n",
       "      <td>BTG2;BCL2A1;PLEK;IRS2;LITAF;IFIH1;PANX1;DRAM1;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSigDB_Hallmark_2020</td>\n",
       "      <td>Complement</td>\n",
       "      <td>27/200</td>\n",
       "      <td>3.220898e-08</td>\n",
       "      <td>5.368163e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.841568</td>\n",
       "      <td>66.270963</td>\n",
       "      <td>FCN1;LRP1;PLEK;LIPA;CA2;CASP3;LAMP2;S100A12;FY...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSigDB_Hallmark_2020</td>\n",
       "      <td>Inflammatory Response</td>\n",
       "      <td>24/200</td>\n",
       "      <td>1.635890e-06</td>\n",
       "      <td>2.044862e-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.343018</td>\n",
       "      <td>44.540108</td>\n",
       "      <td>LYN;IFITM1;BTG2;IL4R;CD82;IL1R1;IFNGR2;ITGB3;F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSigDB_Hallmark_2020</td>\n",
       "      <td>heme Metabolism</td>\n",
       "      <td>23/200</td>\n",
       "      <td>5.533816e-06</td>\n",
       "      <td>5.533816e-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.181358</td>\n",
       "      <td>38.509172</td>\n",
       "      <td>SLC22A4;MPP1;BNIP3L;BTG2;ARHGEF12;NEK7;GDE1;FO...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Gene_set                           Term Overlap       P-value  \\\n",
       "0  MSigDB_Hallmark_2020       IL-6/JAK/STAT3 Signaling   19/87  1.197225e-09   \n",
       "1  MSigDB_Hallmark_2020  TNF-alpha Signaling via NF-kB  27/200  3.220898e-08   \n",
       "2  MSigDB_Hallmark_2020                     Complement  27/200  3.220898e-08   \n",
       "3  MSigDB_Hallmark_2020          Inflammatory Response  24/200  1.635890e-06   \n",
       "4  MSigDB_Hallmark_2020                heme Metabolism  23/200  5.533816e-06   \n",
       "\n",
       "   Adjusted P-value  Old P-value  Old Adjusted P-value  Odds Ratio  \\\n",
       "0      5.986123e-08            0                     0    6.844694   \n",
       "1      5.368163e-07            0                     0    3.841568   \n",
       "2      5.368163e-07            0                     0    3.841568   \n",
       "3      2.044862e-05            0                     0    3.343018   \n",
       "4      5.533816e-05            0                     0    3.181358   \n",
       "\n",
       "   Combined Score                                              Genes  \n",
       "0      140.612324  IL4R;TGFB1;IL1R1;IFNGR1;IL10RB;ITGB3;IFNGR2;IL...  \n",
       "1       66.270963  BTG2;BCL2A1;PLEK;IRS2;LITAF;IFIH1;PANX1;DRAM1;...  \n",
       "2       66.270963  FCN1;LRP1;PLEK;LIPA;CA2;CASP3;LAMP2;S100A12;FY...  \n",
       "3       44.540108  LYN;IFITM1;BTG2;IL4R;CD82;IL1R1;IFNGR2;ITGB3;F...  \n",
       "4       38.509172  SLC22A4;MPP1;BNIP3L;BTG2;ARHGEF12;NEK7;GDE1;FO...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# obj.results stores all results\n",
    "enr.results.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Enrichr Web Service (with `backround` input)\n",
    "\n",
    "NOTE: Missing `Overlap` column in final output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# backgound only reconigized a gene list input.\n",
    "enr_bg = gp.enrichr(gene_list=gene_list,\n",
    "                 gene_sets=['MSigDB_Hallmark_2020','KEGG_2021_Human'], \n",
    "                 # organism='human', # organism argment is ignored because user input a background \n",
    "                 background=\"tests/data/background.txt\",\n",
    "                 outdir=None, # don't write to disk \n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene_set</th>\n",
       "      <th>Term</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Adjusted P-value</th>\n",
       "      <th>Old P-value</th>\n",
       "      <th>Old adjusted P-value</th>\n",
       "      <th>Odds Ratio</th>\n",
       "      <th>Combined Score</th>\n",
       "      <th>Genes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MSigDB_Hallmark_2020</td>\n",
       "      <td>IL-6/JAK/STAT3 Signaling</td>\n",
       "      <td>3.559435e-11</td>\n",
       "      <td>1.779718e-09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.533251</td>\n",
       "      <td>205.300064</td>\n",
       "      <td>IL4R;TGFB1;IL1R1;IFNGR1;IL10RB;ITGB3;IFNGR2;IL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSigDB_Hallmark_2020</td>\n",
       "      <td>TNF-alpha Signaling via NF-kB</td>\n",
       "      <td>3.401526e-10</td>\n",
       "      <td>6.356588e-09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.824842</td>\n",
       "      <td>105.189414</td>\n",
       "      <td>BTG2;BCL2A1;PLEK;IRS2;LITAF;IFIH1;PANX1;DRAM1;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSigDB_Hallmark_2020</td>\n",
       "      <td>Complement</td>\n",
       "      <td>3.813953e-10</td>\n",
       "      <td>6.356588e-09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.796735</td>\n",
       "      <td>104.027683</td>\n",
       "      <td>FCN1;LRP1;PLEK;LIPA;CA2;CASP3;LAMP2;S100A12;FY...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSigDB_Hallmark_2020</td>\n",
       "      <td>Inflammatory Response</td>\n",
       "      <td>3.380686e-08</td>\n",
       "      <td>4.225857e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.197067</td>\n",
       "      <td>72.200480</td>\n",
       "      <td>LYN;IFITM1;BTG2;IL4R;CD82;IL1R1;IFNGR2;ITGB3;F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSigDB_Hallmark_2020</td>\n",
       "      <td>heme Metabolism</td>\n",
       "      <td>8.943634e-08</td>\n",
       "      <td>8.943634e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.111306</td>\n",
       "      <td>66.725423</td>\n",
       "      <td>SLC22A4;MPP1;BNIP3L;BTG2;ARHGEF12;NEK7;GDE1;FO...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Gene_set                           Term       P-value  \\\n",
       "0  MSigDB_Hallmark_2020       IL-6/JAK/STAT3 Signaling  3.559435e-11   \n",
       "1  MSigDB_Hallmark_2020  TNF-alpha Signaling via NF-kB  3.401526e-10   \n",
       "2  MSigDB_Hallmark_2020                     Complement  3.813953e-10   \n",
       "3  MSigDB_Hallmark_2020          Inflammatory Response  3.380686e-08   \n",
       "4  MSigDB_Hallmark_2020                heme Metabolism  8.943634e-08   \n",
       "\n",
       "   Adjusted P-value  Old P-value  Old adjusted P-value  Odds Ratio  \\\n",
       "0      1.779718e-09            0                     0    8.533251   \n",
       "1      6.356588e-09            0                     0    4.824842   \n",
       "2      6.356588e-09            0                     0    4.796735   \n",
       "3      4.225857e-07            0                     0    4.197067   \n",
       "4      8.943634e-07            0                     0    4.111306   \n",
       "\n",
       "   Combined Score                                              Genes  \n",
       "0      205.300064  IL4R;TGFB1;IL1R1;IFNGR1;IL10RB;ITGB3;IFNGR2;IL...  \n",
       "1      105.189414  BTG2;BCL2A1;PLEK;IRS2;LITAF;IFIH1;PANX1;DRAM1;...  \n",
       "2      104.027683  FCN1;LRP1;PLEK;LIPA;CA2;CASP3;LAMP2;S100A12;FY...  \n",
       "3       72.200480  LYN;IFITM1;BTG2;IL4R;CD82;IL1R1;IFNGR2;ITGB3;F...  \n",
       "4       66.725423  SLC22A4;MPP1;BNIP3L;BTG2;ARHGEF12;NEK7;GDE1;FO...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enr_bg.results.head() # "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Over-representation analysis (hypergeometric test) by offline\n",
    "\n",
    "This API **DO NOT** use Enrichr web services.\n",
    "\n",
    "\n",
    "**NOTE**:\n",
    "1. The input gene symbols are **case sensitive**.\n",
    "2. You need to **match the type of the gene identifers** which used in your gene_list input and GMT file. \n",
    "3. Input a .gmt file or gene_set dict object for the argument `gene_sets`\n",
    "\n",
    "For example:\n",
    "```python\n",
    "    gene_sets=\"./data/genes.gmt\",\n",
    "    gene_sets={'A':['gene1', 'gene2',...],\n",
    "               'B':['gene2', 'gene4',...],\n",
    "               ...}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-25 10:46:28,796 [INFO] User defined gene sets is given: ./tests/data/genes.gmt\n",
      "2023-10-25 10:46:28,813 [INFO] Input dict object named with gs_ind_2\n",
      "2023-10-25 10:46:29,289 [WARNING] Input library not found: unknown. Skip\n",
      "2023-10-25 10:46:29,291 [INFO] Run: genes.gmt \n",
      "2023-10-25 10:46:29,293 [INFO] Background is not set! Use all 682 genes in genes.gmt.\n",
      "2023-10-25 10:46:29,302 [INFO] Run: gs_ind_2 \n",
      "2023-10-25 10:46:29,327 [INFO] Done.\n"
     ]
    }
   ],
   "source": [
    "# NOTE: `enrich` instead of `enrichr`\n",
    "enr2 = gp.enrich(gene_list=\"./tests/data/gene_list.txt\", # or gene_list=glist\n",
    "                 gene_sets=[\"./tests/data/genes.gmt\", \"unknown\", kegg ], # kegg is a dict object\n",
    "                 background=None, # or \"hsapiens_gene_ensembl\", or int, or text file, or a list of genes\n",
    "                 outdir=None, \n",
    "                 verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene_set</th>\n",
       "      <th>Term</th>\n",
       "      <th>Overlap</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Adjusted P-value</th>\n",
       "      <th>Odds Ratio</th>\n",
       "      <th>Combined Score</th>\n",
       "      <th>Genes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>genes.gmt</td>\n",
       "      <td>BvA_UpIN_A</td>\n",
       "      <td>8/139</td>\n",
       "      <td>0.457390</td>\n",
       "      <td>0.568432</td>\n",
       "      <td>1.161982</td>\n",
       "      <td>0.908925</td>\n",
       "      <td>PCSK6;MAP3K5;MBOAT2;MSRB2;IQGAP2;HAL;PADI2;IL1R1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>genes.gmt</td>\n",
       "      <td>BvA_UpIN_B</td>\n",
       "      <td>12/130</td>\n",
       "      <td>0.026744</td>\n",
       "      <td>0.187208</td>\n",
       "      <td>2.160059</td>\n",
       "      <td>7.822534</td>\n",
       "      <td>FAM65B;MBNL3;GPX8;DYSF;KCTD12;HEBP1;SUOX;ARHGD...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>genes.gmt</td>\n",
       "      <td>CvA_UpIN_A</td>\n",
       "      <td>1/12</td>\n",
       "      <td>0.481190</td>\n",
       "      <td>0.568432</td>\n",
       "      <td>2.266479</td>\n",
       "      <td>1.657913</td>\n",
       "      <td>MBOAT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>genes.gmt</td>\n",
       "      <td>DvA_UpIN_A</td>\n",
       "      <td>16/284</td>\n",
       "      <td>0.426669</td>\n",
       "      <td>0.568432</td>\n",
       "      <td>1.127395</td>\n",
       "      <td>0.960255</td>\n",
       "      <td>PCSK6;FXYD6;IFNGR2;MAP3K5;MBOAT2;VNN1;IQGAP2;H...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>genes.gmt</td>\n",
       "      <td>DvA_UpIN_D</td>\n",
       "      <td>13/236</td>\n",
       "      <td>0.487227</td>\n",
       "      <td>0.568432</td>\n",
       "      <td>1.084567</td>\n",
       "      <td>0.779830</td>\n",
       "      <td>GNB4;FAM198B;FAM65B;TXNDC5;GLIPR2;MBNL3;GPX8;D...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gene_set        Term Overlap   P-value  Adjusted P-value  Odds Ratio  \\\n",
       "0  genes.gmt  BvA_UpIN_A   8/139  0.457390          0.568432    1.161982   \n",
       "1  genes.gmt  BvA_UpIN_B  12/130  0.026744          0.187208    2.160059   \n",
       "2  genes.gmt  CvA_UpIN_A    1/12  0.481190          0.568432    2.266479   \n",
       "3  genes.gmt  DvA_UpIN_A  16/284  0.426669          0.568432    1.127395   \n",
       "4  genes.gmt  DvA_UpIN_D  13/236  0.487227          0.568432    1.084567   \n",
       "\n",
       "   Combined Score                                              Genes  \n",
       "0        0.908925   PCSK6;MAP3K5;MBOAT2;MSRB2;IQGAP2;HAL;PADI2;IL1R1  \n",
       "1        7.822534  FAM65B;MBNL3;GPX8;DYSF;KCTD12;HEBP1;SUOX;ARHGD...  \n",
       "2        1.657913                                             MBOAT2  \n",
       "3        0.960255  PCSK6;FXYD6;IFNGR2;MAP3K5;MBOAT2;VNN1;IQGAP2;H...  \n",
       "4        0.779830  GNB4;FAM198B;FAM65B;TXNDC5;GLIPR2;MBNL3;GPX8;D...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enr2.results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### About Background genes \n",
    "\n",
    "By default, all genes in the `gene_sets` input will be used as background. \n",
    "\n",
    "However, a better background genes would be the following:\n",
    "\n",
    "\n",
    "1. (Recommended) Input a list of background genes: ['gene1', 'gene2',...]\n",
    "    - The background gene list is defined by your experment. e.g. the expressed genes in your RNA-seq.\n",
    "    - The gene identifer in gmt/dict should be the same type to the backgound genes.\n",
    "\n",
    "2. Specify a number: e.g. 20000. (the number of total expressed genes).\n",
    "    - This works, but not recommend. It assumes that all your genes could be found in background.\n",
    "    - If genes exist in gmt but not included in background provided, they will affect the significance of the statistical test.\n",
    "\n",
    "3. Set a Biomart dataset name: e.g. \"hsapiens_gene_ensembl\"\n",
    "    - The background will use all annotated genes from the `BioMart datasets` you've choosen.\n",
    "    - The program will try to retrieve the background information automatically.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show top 5 terms of each gene_set ranked by \"Adjusted P-value\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simple plotting function\n",
    "from gseapy import barplot, dotplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# categorical scatterplot\n",
    "ax = dotplot(enr.results, \n",
    "              column=\"Adjusted P-value\", \n",
    "              x='Gene_set', # set x axis, so you could do a multi-sample/library comparsion\n",
    "              size=10, \n",
    "              top_term=5,\n",
    "              figsize=(3,5), \n",
    "              title = \"KEGG\",\n",
    "              xticklabels_rot=45, # rotate xtick labels\n",
    "              show_ring=True, # set to False to revmove outer ring\n",
    "              marker='o',\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# categorical scatterplot\n",
    "ax = barplot(enr.results, \n",
    "              column=\"Adjusted P-value\", \n",
    "              group='Gene_set', # set group, so you could do a multi-sample/library comparsion\n",
    "              size=10, \n",
    "              top_term=5,\n",
    "              figsize=(3,5), \n",
    "              #color=['darkred', 'darkblue'] # set colors for group\n",
    "              color = {'KEGG_2021_Human': 'salmon', 'MSigDB_Hallmark_2020':'darkblue'}\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to save your figure, make sure that ``ofname`` is not None\n",
    "ax = dotplot(enr.res2d, title='KEGG_2021_Human',cmap='viridis_r', size=10, figsize=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to save your figure, make sure that ``ofname`` is not None\n",
    "ax = barplot(enr.res2d,title='KEGG_2021_Human', figsize=(4, 5), color='darkred')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4cec30f1-df1f-4a67-a3f1-a37e207efbd3"
    }
   },
   "source": [
    "### Command line usage "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "3bf3a986-f05a-4852-a7a2-e63eb12c9236"
    }
   },
   "source": [
    "the option **-v** will print out the progress of your job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !gseapy enrichr -i ./data/gene_list.txt \\\n",
    "#                 -g GO_Biological_Process_2017 \\\n",
    "#                 -v -o test/enrichr_BP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "09b58c6b-f78a-45b0-834a-f92e3b8d1f62"
    }
   },
   "source": [
    "## Prerank example\n",
    "\n",
    "### Assign prerank() with \n",
    "\n",
    "- pd.DataFrame: Only contains two columns, or one cloumn with gene_name indexed \n",
    "- pd.Series \n",
    "- a txt file: \n",
    "  - **GSEApy will skip any data after \"#\".** \n",
    "  - Do not include header in your gene list !  \n",
    "\n",
    "\n",
    "#### NOTE: UPCASES for gene symbols by Default\n",
    "\n",
    "1. Gene symbols are all \"UPCASES\" in the Enrichr Libaries. You should convert your input gene identifier to \"UPCASES\" first.\n",
    "2. If input `gmt`, `dict` object, please refer to `1.2 Mouse gene symbols maps to Human, or Vice Versa` (in this page) to convert gene identifier\n",
    "\n",
    "\n",
    "\n",
    "#### Supported gene_sets input\n",
    "For example:\n",
    "```python\n",
    "    gene_sets=\"KEGG_2016\",\n",
    "    gene_sets=\"KEGG_2016,KEGG2013\",\n",
    "    gene_sets=\"./data/genes.gmt\",\n",
    "    gene_sets=[\"KEGG_2016\",\"./data/genes.gmt\"],\n",
    "    gene_sets={'A':['gene1', 'gene2',...],\n",
    "               'B':['gene2', 'gene4',...],\n",
    "               ...}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATXN1</th>\n",
       "      <td>16.456753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN4</th>\n",
       "      <td>13.989493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALM1</th>\n",
       "      <td>13.745533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DLG4</th>\n",
       "      <td>12.796588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRE11A</th>\n",
       "      <td>12.787631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                1\n",
       "0                \n",
       "ATXN1   16.456753\n",
       "UBQLN4  13.989493\n",
       "CALM1   13.745533\n",
       "DLG4    12.796588\n",
       "MRE11A  12.787631"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnk = pd.read_csv(\"./tests/data/temp.rnk\", header=None, index_col=0, sep=\"\\t\")\n",
    "rnk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22922, 1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnk.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-25 10:46:30,863 [WARNING] Duplicated values found in preranked stats: 4.97% of genes\n",
      "The order of those genes will be arbitrary, which may produce unexpected results.\n",
      "2023-10-25 10:46:30,864 [INFO] Parsing data files for GSEA.............................\n",
      "2023-10-25 10:46:30,865 [INFO] Enrichr library gene sets already downloaded in: /home/fangzq/.cache/gseapy, use local file\n",
      "2023-10-25 10:46:30,902 [INFO] 0001 gene_sets have been filtered out when max_size=1000 and min_size=5\n",
      "2023-10-25 10:46:30,903 [INFO] 0292 gene_sets used for further statistical testing.....\n",
      "2023-10-25 10:46:30,903 [INFO] Start to run GSEA...Might take a while..................\n",
      "2023-10-25 10:46:43,563 [INFO] Congratulations. GSEApy runs successfully................\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# # run prerank\n",
    "# # enrichr libraries are supported by prerank module. Just provide the name\n",
    "# # use 4 process to acceralate the permutation speed\n",
    "pre_res = gp.prerank(rnk=\"./tests/data/temp.rnk\", # or rnk = rnk,\n",
    "                     gene_sets='KEGG_2016', \n",
    "                     threads=4,\n",
    "                     min_size=5,\n",
    "                     max_size=1000,\n",
    "                     permutation_num=1000, # reduce number to speed up testing\n",
    "                     outdir=None, # don't write to disk \n",
    "                     seed=6, \n",
    "                     verbose=True, # see what's going on behind the scenes\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to generate your GSEA plot inside python console  \n",
    "Visualize it using ``gseaplot``  \n",
    "\n",
    "Make sure that ``ofname`` is not None, if you want to save your figure to the disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Term</th>\n",
       "      <th>ES</th>\n",
       "      <th>NES</th>\n",
       "      <th>NOM p-val</th>\n",
       "      <th>FDR q-val</th>\n",
       "      <th>FWER p-val</th>\n",
       "      <th>Tag %</th>\n",
       "      <th>Gene %</th>\n",
       "      <th>Lead_genes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>prerank</td>\n",
       "      <td>Adherens junction Homo sapiens hsa04520</td>\n",
       "      <td>0.784625</td>\n",
       "      <td>1.912548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47/74</td>\n",
       "      <td>10.37%</td>\n",
       "      <td>CTNNB1;EGFR;RAC1;TGFBR1;SMAD4;MET;EP300;CDC42;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>prerank</td>\n",
       "      <td>Glioma Homo sapiens hsa05214</td>\n",
       "      <td>0.784678</td>\n",
       "      <td>1.906706</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52/65</td>\n",
       "      <td>16.29%</td>\n",
       "      <td>CALM1;GRB2;EGFR;PRKCA;KRAS;HRAS;TP53;MAPK1;PRK...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>prerank</td>\n",
       "      <td>Estrogen signaling pathway Homo sapiens hsa04915</td>\n",
       "      <td>0.766347</td>\n",
       "      <td>1.897957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74/99</td>\n",
       "      <td>16.57%</td>\n",
       "      <td>CALM1;PRKACA;GRB2;SP1;EGFR;KRAS;HRAS;HSP90AB1;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>prerank</td>\n",
       "      <td>Thyroid hormone signaling pathway Homo sapiens...</td>\n",
       "      <td>0.7577</td>\n",
       "      <td>1.891815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84/118</td>\n",
       "      <td>16.29%</td>\n",
       "      <td>CTNNB1;PRKACA;PRKCA;KRAS;NOTCH1;EP300;CREBBP;H...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>prerank</td>\n",
       "      <td>Long-term potentiation Homo sapiens hsa04720</td>\n",
       "      <td>0.778249</td>\n",
       "      <td>1.888739</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42/66</td>\n",
       "      <td>9.01%</td>\n",
       "      <td>CALM1;PRKACA;PRKCA;KRAS;EP300;CREBBP;HRAS;PRKA...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name                                               Term        ES  \\\n",
       "0  prerank            Adherens junction Homo sapiens hsa04520  0.784625   \n",
       "1  prerank                       Glioma Homo sapiens hsa05214  0.784678   \n",
       "2  prerank   Estrogen signaling pathway Homo sapiens hsa04915  0.766347   \n",
       "3  prerank  Thyroid hormone signaling pathway Homo sapiens...    0.7577   \n",
       "4  prerank       Long-term potentiation Homo sapiens hsa04720  0.778249   \n",
       "\n",
       "        NES NOM p-val FDR q-val FWER p-val   Tag %  Gene %  \\\n",
       "0  1.912548       0.0       0.0        0.0   47/74  10.37%   \n",
       "1  1.906706       0.0       0.0        0.0   52/65  16.29%   \n",
       "2  1.897957       0.0       0.0        0.0   74/99  16.57%   \n",
       "3  1.891815       0.0       0.0        0.0  84/118  16.29%   \n",
       "4  1.888739       0.0       0.0        0.0   42/66   9.01%   \n",
       "\n",
       "                                          Lead_genes  \n",
       "0  CTNNB1;EGFR;RAC1;TGFBR1;SMAD4;MET;EP300;CDC42;...  \n",
       "1  CALM1;GRB2;EGFR;PRKCA;KRAS;HRAS;TP53;MAPK1;PRK...  \n",
       "2  CALM1;PRKACA;GRB2;SP1;EGFR;KRAS;HRAS;HSP90AB1;...  \n",
       "3  CTNNB1;PRKACA;PRKCA;KRAS;NOTCH1;EP300;CREBBP;H...  \n",
       "4  CALM1;PRKACA;PRKCA;KRAS;EP300;CREBBP;HRAS;PRKA...  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_res.res2d.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## easy way\n",
    "terms = pre_res.res2d.Term\n",
    "axs = pre_res.plot(terms=terms[1]) # v1.0.5\n",
    "# to make more control on the plot, use\n",
    "# from gseapy import gseaplot\n",
    "# gseaplot(rank_metric=pre_res.ranking, term=terms[0], ofname='your.plot.pdf', **pre_res.results[terms[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or multi pathway in one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x396 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = pre_res.plot(terms=terms[1:5], \n",
    "                   #legend_kws={'loc': (1.2, 0)}, # set the legend loc\n",
    "                   show_ranking=True, # whether to show the second yaxis\n",
    "                   figsize=(3,4)\n",
    "                  )\n",
    "# or use this to have more control on the plot\n",
    "# from gseapy import gseaplot2\n",
    "# terms = pre_res.res2d.Term[1:5]\n",
    "# hits = [pre_res.results[t]['hits'] for t in terms]\n",
    "# runes = [pre_res.results[t]['RES'] for t in terms]\n",
    "# fig = gseaplot2(terms=terms, ress=runes, hits=hits, \n",
    "#               rank_metric=gs_res.ranking, \n",
    "#               legend_kws={'loc': (1.2, 0)}, # set the legend loc\n",
    "#               figsize=(4,5)) # rank_metric=pre_res.ranking "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`dotplot` for GSEA resutls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gseapy import dotplot\n",
    "# to save your figure, make sure that ``ofname`` is not None\n",
    "ax = dotplot(pre_res.res2d, \n",
    "             column=\"FDR q-val\",\n",
    "             title='KEGG_2016',\n",
    "             cmap=plt.cm.viridis, \n",
    "             size=6, # adjust dot size\n",
    "             figsize=(4,5), cutoff=0.25, show_ring=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Network Visualization\n",
    "\n",
    "- use `enrichment_map` to build network\n",
    "- save the `nodes` and `edges`. They could be used for `cytoscape` visualization.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gseapy import enrichment_map\n",
    "# return two dataframe\n",
    "nodes, edges = enrichment_map(pre_res.res2d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build graph\n",
    "G = nx.from_pandas_edgelist(edges, \n",
    "                            source='src_idx', \n",
    "                            target='targ_idx', \n",
    "                            edge_attr=['jaccard_coef', 'overlap_coef', 'overlap_genes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# init node cooridnates\n",
    "pos=nx.layout.spiral_layout(G)\n",
    "#node_size = nx.get_node_attributes()\n",
    "# draw node\n",
    "nx.draw_networkx_nodes(G, \n",
    "                       pos=pos, \n",
    "                       cmap=plt.cm.RdYlBu, \n",
    "                       node_color=list(nodes.NES),\n",
    "                       node_size=list(nodes.Hits_ratio *1000))\n",
    "# draw node label\n",
    "nx.draw_networkx_labels(G, \n",
    "                        pos=pos, \n",
    "                        labels=nodes.Term.to_dict())\n",
    "# draw edge\n",
    "edge_weight = nx.get_edge_attributes(G, 'jaccard_coef').values()\n",
    "nx.draw_networkx_edges(G, \n",
    "                       pos=pos, \n",
    "                       width=list(map(lambda x: x*10, edge_weight)), \n",
    "                       edge_color='#CDDBD4')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "5eb96091-8a46-4f40-b5e6-d614be61d8f8"
    }
   },
   "source": [
    "### Command line usage "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "6d1d1e0f-5cff-4b36-bf17-ea673072466a"
    }
   },
   "source": [
    "You may also want to use prerank in command line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !gseapy prerank -r temp.rnk -g temp.gmt -o prerank_report_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e131526a-2501-4546-b82b-49037c1397c0"
    }
   },
   "source": [
    "## GSEA Example\n",
    "\n",
    "\n",
    "### Inputs \n",
    "\n",
    "Assign gsea() \n",
    "\n",
    "- data with: \n",
    "  - pandas DataFrame\n",
    "  - .gct format file, or a text file\n",
    "\n",
    "- cls with: \n",
    "  - a list\n",
    "  - a .cls format file \n",
    "\n",
    "- gene_sets with:\n",
    "\n",
    "```python\n",
    "    gene_sets=\"KEGG_2016\",\n",
    "    gene_sets=\"KEGG_2016,KEGG2013\",\n",
    "    gene_sets=\"./data/genes.gmt\",\n",
    "    gene_sets=[\"KEGG_2016\",\"./data/genes.gmt\"],\n",
    "    gene_sets={'A':['gene1', 'gene2',...],\n",
    "               'B':['gene2', 'gene4',...],\n",
    "               ...}\n",
    "```\n",
    "\n",
    "\n",
    "#### NOTE: UPCASES for gene symbols by Default\n",
    "\n",
    "1. Gene symbols are all \"UPCASES\" in the Enrichr Libaries. You should convert your input gene identifier to \"UPCASES\" first.\n",
    "2. If input `gmt`, `dict` object, please refer to `1.2 Mouse gene symbols maps to Human, or Vice Versa` (in this page) to convert gene identifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gseapy as gp\n",
    "phenoA, phenoB, class_vector =  gp.parser.gsea_cls_parser(\"./tests/extdata/Leukemia.cls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML']\n"
     ]
    }
   ],
   "source": [
    "#class_vector used to indicate group attributes for each sample\n",
    "print(class_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene</th>\n",
       "      <th>NAME</th>\n",
       "      <th>ALL_1</th>\n",
       "      <th>ALL_2</th>\n",
       "      <th>ALL_3</th>\n",
       "      <th>ALL_4</th>\n",
       "      <th>ALL_5</th>\n",
       "      <th>ALL_6</th>\n",
       "      <th>ALL_7</th>\n",
       "      <th>ALL_8</th>\n",
       "      <th>...</th>\n",
       "      <th>AML_15</th>\n",
       "      <th>AML_16</th>\n",
       "      <th>AML_17</th>\n",
       "      <th>AML_18</th>\n",
       "      <th>AML_19</th>\n",
       "      <th>AML_20</th>\n",
       "      <th>AML_21</th>\n",
       "      <th>AML_22</th>\n",
       "      <th>AML_23</th>\n",
       "      <th>AML_24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAPK3</td>\n",
       "      <td>1000_at</td>\n",
       "      <td>1633.6</td>\n",
       "      <td>2455.0</td>\n",
       "      <td>866.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3159.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1580.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1826.0</td>\n",
       "      <td>2849.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>3672.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>2188.0</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>1934.0</td>\n",
       "      <td>13154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TIE1</td>\n",
       "      <td>1001_at</td>\n",
       "      <td>284.4</td>\n",
       "      <td>159.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>1187.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1556.0</td>\n",
       "      <td>893.0</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>941.0</td>\n",
       "      <td>1398.0</td>\n",
       "      <td>-502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CYP2C19</td>\n",
       "      <td>1002_f_at</td>\n",
       "      <td>285.8</td>\n",
       "      <td>114.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>-43.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>-88.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-177.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>-359.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-464.0</td>\n",
       "      <td>-127.0</td>\n",
       "      <td>-279.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>509.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CXCR5</td>\n",
       "      <td>1003_s_at</td>\n",
       "      <td>-126.6</td>\n",
       "      <td>-388.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>-915.0</td>\n",
       "      <td>-439.0</td>\n",
       "      <td>-371.0</td>\n",
       "      <td>-448.0</td>\n",
       "      <td>-862.0</td>\n",
       "      <td>...</td>\n",
       "      <td>237.0</td>\n",
       "      <td>-834.0</td>\n",
       "      <td>-1940.0</td>\n",
       "      <td>-684.0</td>\n",
       "      <td>-1236.0</td>\n",
       "      <td>-1561.0</td>\n",
       "      <td>-895.0</td>\n",
       "      <td>-1016.0</td>\n",
       "      <td>-2238.0</td>\n",
       "      <td>-1362.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CXCR5</td>\n",
       "      <td>1004_at</td>\n",
       "      <td>-83.3</td>\n",
       "      <td>33.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>...</td>\n",
       "      <td>86.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>-153.0</td>\n",
       "      <td>-50.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>386.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gene       NAME   ALL_1   ALL_2  ALL_3   ALL_4   ALL_5   ALL_6   ALL_7  \\\n",
       "0    MAPK3    1000_at  1633.6  2455.0  866.0  1000.0  3159.0  1998.0  1580.0   \n",
       "1     TIE1    1001_at   284.4   159.0  173.0   216.0  1187.0   647.0   352.0   \n",
       "2  CYP2C19  1002_f_at   285.8   114.0  429.0   -43.0    18.0   366.0   119.0   \n",
       "3    CXCR5  1003_s_at  -126.6  -388.0  143.0  -915.0  -439.0  -371.0  -448.0   \n",
       "4    CXCR5    1004_at   -83.3    33.0  195.0    85.0    54.0    -6.0    55.0   \n",
       "\n",
       "    ALL_8  ...  AML_15  AML_16  AML_17  AML_18  AML_19  AML_20  AML_21  \\\n",
       "0  1955.0  ...  1826.0  2849.0  2980.0  1442.0  3672.0   294.0  2188.0   \n",
       "1  1224.0  ...  1556.0   893.0  1278.0   301.0   797.0   248.0   167.0   \n",
       "2   -88.0  ...  -177.0    64.0  -359.0    68.0     2.0  -464.0  -127.0   \n",
       "3  -862.0  ...   237.0  -834.0 -1940.0  -684.0 -1236.0 -1561.0  -895.0   \n",
       "4   101.0  ...    86.0    -5.0   487.0   102.0    33.0  -153.0   -50.0   \n",
       "\n",
       "   AML_22  AML_23   AML_24  \n",
       "0  1245.0  1934.0  13154.0  \n",
       "1   941.0  1398.0   -502.0  \n",
       "2  -279.0   301.0    509.0  \n",
       "3 -1016.0 -2238.0  -1362.0  \n",
       "4   257.0   439.0    386.0  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_exp = pd.read_csv(\"./tests/extdata/Leukemia_hgu95av2.trim.txt\", sep=\"\\t\")\n",
    "gene_exp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positively correlated:  ALL\n"
     ]
    }
   ],
   "source": [
    "print(\"positively correlated: \", phenoA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negtively correlated:  AML\n"
     ]
    }
   ],
   "source": [
    "print(\"negtively correlated: \", phenoB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-25 10:46:47,125 [WARNING] Found duplicated gene names, values averaged by gene names!\n"
     ]
    }
   ],
   "source": [
    "# run gsea\n",
    "# enrichr libraries are supported by gsea module. Just provide the name\n",
    "gs_res = gp.gsea(data=gene_exp, # or data='./P53_resampling_data.txt'\n",
    "                 gene_sets='./tests/extdata/h.all.v7.0.symbols.gmt', # or enrichr library names\n",
    "                 cls= \"./tests/extdata/Leukemia.cls\", # cls=class_vector\n",
    "                 # set permutation_type to phenotype if samples >=15\n",
    "                 permutation_type='phenotype', \n",
    "                 permutation_num=1000, # reduce number to speed up test\n",
    "                 outdir=None,  # do not write output to disk\n",
    "                 method='signal_to_noise',\n",
    "                 threads=4, seed= 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can set `pheno_pos`, and `pheno_neg` mannually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-25 10:46:50,381 [WARNING] Found duplicated gene names, values averaged by gene names!\n"
     ]
    }
   ],
   "source": [
    "# example\n",
    "from gseapy import GSEA\n",
    "gs = GSEA(data=gene_exp,\n",
    "         gene_sets='KEGG_2016', \n",
    "         classes = class_vector, # cls=class_vector\n",
    "         # set permutation_type to phenotype if samples >=15\n",
    "         permutation_type='phenotype', \n",
    "         permutation_num=1000, # reduce number to speed up test\n",
    "         outdir=None, \n",
    "         method='signal_to_noise',\n",
    "         threads=4, seed= 8)\n",
    "gs.pheno_pos = \"AML\"\n",
    "gs.pheno_neg = \"ALL\"\n",
    "gs.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show the gsea plots\n",
    "\n",
    "The **gsea** module will generate heatmap for genes in each gene sets in the backgroud.  \n",
    "But if you need to do it yourself, use the code below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x396 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "terms = gs_res.res2d.Term\n",
    "axs = gs_res.plot(terms[:5], show_ranking=False, legend_kws={'loc': (1.05, 0)}, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# or use \n",
    "# from gseapy import gseaplot2\n",
    "\n",
    "# # multi in one\n",
    "# terms = gs_res.res2d.Term[:5]\n",
    "# hits = [gs_res.results[t]['hits'] for t in terms]\n",
    "# runes = [gs_res.results[t]['RES'] for t in terms]\n",
    "# fig = gseaplot2(terms=terms, ress=runes, hits=hits, \n",
    "#               rank_metric=gs_res.ranking, \n",
    "#               legend_kws={'loc': (1.2, 0)}, # set the legend loc\n",
    "#               figsize=(4,5)) # rank_metric=pre_res.ranking "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gseapy import heatmap\n",
    "# plotting heatmap\n",
    "i = 2\n",
    "genes = gs_res.res2d.Lead_genes[i].split(\";\")\n",
    "# Make sure that ``ofname`` is not None, if you want to save your figure to disk\n",
    "ax = heatmap(df = gs_res.heatmat.loc[genes], z_score=0, title=terms[i], figsize=(14,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALL_1</th>\n",
       "      <th>ALL_2</th>\n",
       "      <th>ALL_3</th>\n",
       "      <th>ALL_4</th>\n",
       "      <th>ALL_5</th>\n",
       "      <th>ALL_6</th>\n",
       "      <th>ALL_7</th>\n",
       "      <th>ALL_8</th>\n",
       "      <th>ALL_9</th>\n",
       "      <th>ALL_10</th>\n",
       "      <th>...</th>\n",
       "      <th>AML_15</th>\n",
       "      <th>AML_16</th>\n",
       "      <th>AML_17</th>\n",
       "      <th>AML_18</th>\n",
       "      <th>AML_19</th>\n",
       "      <th>AML_20</th>\n",
       "      <th>AML_21</th>\n",
       "      <th>AML_22</th>\n",
       "      <th>AML_23</th>\n",
       "      <th>AML_24</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LEF1</th>\n",
       "      <td>8544.10</td>\n",
       "      <td>12552.0</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>15265.0</td>\n",
       "      <td>7446.0</td>\n",
       "      <td>6991.0</td>\n",
       "      <td>15520.0</td>\n",
       "      <td>13114.0</td>\n",
       "      <td>22604.0</td>\n",
       "      <td>21795.0</td>\n",
       "      <td>...</td>\n",
       "      <td>682.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>-348.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>-242.0</td>\n",
       "      <td>-47.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKP2</th>\n",
       "      <td>23.80</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-95.0</td>\n",
       "      <td>-71.0</td>\n",
       "      <td>-65.0</td>\n",
       "      <td>-547.0</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>-162.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-865.0</td>\n",
       "      <td>-642.0</td>\n",
       "      <td>-1005.0</td>\n",
       "      <td>-413.0</td>\n",
       "      <td>-733.0</td>\n",
       "      <td>-812.0</td>\n",
       "      <td>-464.0</td>\n",
       "      <td>-490.0</td>\n",
       "      <td>-333.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUL1</th>\n",
       "      <td>1712.75</td>\n",
       "      <td>3309.0</td>\n",
       "      <td>1273.5</td>\n",
       "      <td>1726.5</td>\n",
       "      <td>947.5</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>2524.5</td>\n",
       "      <td>1882.5</td>\n",
       "      <td>2684.5</td>\n",
       "      <td>...</td>\n",
       "      <td>851.5</td>\n",
       "      <td>614.5</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>523.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>1214.5</td>\n",
       "      <td>770.0</td>\n",
       "      <td>2088.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDAC2</th>\n",
       "      <td>4542.90</td>\n",
       "      <td>6030.0</td>\n",
       "      <td>1195.0</td>\n",
       "      <td>9368.0</td>\n",
       "      <td>2281.0</td>\n",
       "      <td>3407.0</td>\n",
       "      <td>3175.0</td>\n",
       "      <td>3962.0</td>\n",
       "      <td>2616.0</td>\n",
       "      <td>6848.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1072.0</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>1545.0</td>\n",
       "      <td>1653.0</td>\n",
       "      <td>2328.0</td>\n",
       "      <td>1061.0</td>\n",
       "      <td>1571.0</td>\n",
       "      <td>1749.0</td>\n",
       "      <td>2942.0</td>\n",
       "      <td>1174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GNAI1</th>\n",
       "      <td>588.50</td>\n",
       "      <td>163.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1136.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>...</td>\n",
       "      <td>470.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>-163.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>-331.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>-80.0</td>\n",
       "      <td>-94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAML1</th>\n",
       "      <td>871.40</td>\n",
       "      <td>1871.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>1589.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>1364.0</td>\n",
       "      <td>2494.0</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>1538.0</td>\n",
       "      <td>1946.0</td>\n",
       "      <td>...</td>\n",
       "      <td>390.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>962.0</td>\n",
       "      <td>997.0</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>609.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>1056.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WNT1</th>\n",
       "      <td>-872.50</td>\n",
       "      <td>-875.0</td>\n",
       "      <td>-1012.0</td>\n",
       "      <td>-535.0</td>\n",
       "      <td>-654.0</td>\n",
       "      <td>-694.0</td>\n",
       "      <td>-421.0</td>\n",
       "      <td>-827.0</td>\n",
       "      <td>-770.0</td>\n",
       "      <td>-1001.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-2506.0</td>\n",
       "      <td>-2791.0</td>\n",
       "      <td>-2249.0</td>\n",
       "      <td>-1201.0</td>\n",
       "      <td>-1819.0</td>\n",
       "      <td>-2599.0</td>\n",
       "      <td>-995.0</td>\n",
       "      <td>-1861.0</td>\n",
       "      <td>-1835.0</td>\n",
       "      <td>-714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDAC5</th>\n",
       "      <td>2137.20</td>\n",
       "      <td>2374.0</td>\n",
       "      <td>1651.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>3132.0</td>\n",
       "      <td>2279.0</td>\n",
       "      <td>2314.0</td>\n",
       "      <td>2349.0</td>\n",
       "      <td>2376.0</td>\n",
       "      <td>5455.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1215.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>1923.0</td>\n",
       "      <td>1927.0</td>\n",
       "      <td>2872.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>1629.0</td>\n",
       "      <td>2763.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AXIN1</th>\n",
       "      <td>-433.50</td>\n",
       "      <td>-722.0</td>\n",
       "      <td>-808.0</td>\n",
       "      <td>-623.0</td>\n",
       "      <td>-1167.0</td>\n",
       "      <td>-326.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>-661.0</td>\n",
       "      <td>-1315.0</td>\n",
       "      <td>-1991.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-2590.0</td>\n",
       "      <td>-2417.0</td>\n",
       "      <td>-1321.0</td>\n",
       "      <td>-466.0</td>\n",
       "      <td>-1628.0</td>\n",
       "      <td>-1910.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-2951.0</td>\n",
       "      <td>-1666.0</td>\n",
       "      <td>471.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NUMB</th>\n",
       "      <td>1033.60</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>2106.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>1549.0</td>\n",
       "      <td>...</td>\n",
       "      <td>491.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>1436.0</td>\n",
       "      <td>841.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>1158.0</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>2109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTCH1</th>\n",
       "      <td>352.60</td>\n",
       "      <td>86.0</td>\n",
       "      <td>372.5</td>\n",
       "      <td>-326.5</td>\n",
       "      <td>181.5</td>\n",
       "      <td>413.5</td>\n",
       "      <td>337.0</td>\n",
       "      <td>94.5</td>\n",
       "      <td>534.0</td>\n",
       "      <td>354.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-62.5</td>\n",
       "      <td>-454.0</td>\n",
       "      <td>-487.0</td>\n",
       "      <td>-185.0</td>\n",
       "      <td>-663.5</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>-58.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>178.5</td>\n",
       "      <td>1061.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ALL_1    ALL_2   ALL_3    ALL_4   ALL_5   ALL_6    ALL_7    ALL_8  \\\n",
       "Gene                                                                         \n",
       "LEF1   8544.10  12552.0  2869.0  15265.0  7446.0  6991.0  15520.0  13114.0   \n",
       "SKP2     23.80    -45.0   -95.0    -71.0   -65.0  -547.0    -24.0    230.0   \n",
       "CUL1   1712.75   3309.0  1273.5   1726.5   947.5  2160.0   2065.0   2524.5   \n",
       "HDAC2  4542.90   6030.0  1195.0   9368.0  2281.0  3407.0   3175.0   3962.0   \n",
       "GNAI1   588.50    163.0   364.0    882.0  1317.0    17.0    518.0     89.0   \n",
       "MAML1   871.40   1871.0   578.0   1589.0  1448.0  1364.0   2494.0   1989.0   \n",
       "WNT1   -872.50   -875.0 -1012.0   -535.0  -654.0  -694.0   -421.0   -827.0   \n",
       "HDAC5  2137.20   2374.0  1651.0   2012.0  3132.0  2279.0   2314.0   2349.0   \n",
       "AXIN1  -433.50   -722.0  -808.0   -623.0 -1167.0  -326.0    448.0   -661.0   \n",
       "NUMB   1033.60   1474.0   600.0   2106.0  1239.0  1430.0   1468.0   1594.0   \n",
       "PTCH1   352.60     86.0   372.5   -326.5   181.5   413.5    337.0     94.5   \n",
       "\n",
       "         ALL_9   ALL_10  ...  AML_15  AML_16  AML_17  AML_18  AML_19  AML_20  \\\n",
       "Gene                     ...                                                   \n",
       "LEF1   22604.0  21795.0  ...   682.0   152.0  -348.0    30.0   210.0   350.0   \n",
       "SKP2     159.0   -162.0  ...  -865.0  -642.0 -1005.0  -413.0  -733.0  -812.0   \n",
       "CUL1    1882.5   2684.5  ...   851.5   614.5  1560.0   523.0   952.0   935.0   \n",
       "HDAC2   2616.0   6848.0  ...  1072.0  1918.0  1545.0  1653.0  2328.0  1061.0   \n",
       "GNAI1   1136.0    816.0  ...   470.0   313.0  -163.0   210.0   684.0  -331.0   \n",
       "MAML1   1538.0   1946.0  ...   390.0   233.0  1075.0   962.0   997.0  1316.0   \n",
       "WNT1    -770.0  -1001.0  ... -2506.0 -2791.0 -2249.0 -1201.0 -1819.0 -2599.0   \n",
       "HDAC5   2376.0   5455.0  ...  1215.0  1024.0   760.0  1368.0  1923.0  1927.0   \n",
       "AXIN1  -1315.0  -1991.0  ... -2590.0 -2417.0 -1321.0  -466.0 -1628.0 -1910.0   \n",
       "NUMB    1014.0   1549.0  ...   491.0   342.0  1594.0   990.0  1436.0   841.0   \n",
       "PTCH1    534.0    354.5  ...   -62.5  -454.0  -487.0  -185.0  -663.5  -150.0   \n",
       "\n",
       "       AML_21  AML_22  AML_23  AML_24  \n",
       "Gene                                   \n",
       "LEF1   -242.0   -47.0   176.0    14.0  \n",
       "SKP2   -464.0  -490.0  -333.0     7.0  \n",
       "CUL1    646.0  1214.5   770.0  2088.5  \n",
       "HDAC2  1571.0  1749.0  2942.0  1174.0  \n",
       "GNAI1   115.0    55.0   -80.0   -94.0  \n",
       "MAML1    48.0   609.0  2090.0  1056.0  \n",
       "WNT1   -995.0 -1861.0 -1835.0  -714.0  \n",
       "HDAC5  2872.0   848.0  1629.0  2763.0  \n",
       "AXIN1    93.0 -2951.0 -1666.0   471.0  \n",
       "NUMB    352.0  1158.0  1541.0  2109.0  \n",
       "PTCH1   -58.0   252.0   178.5  1061.0  \n",
       "\n",
       "[11 rows x 48 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_res.heatmat.loc[genes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gseapy import dotplot\n",
    "# to save your figure, make sure that ``ofname`` is not None\n",
    "ax = dotplot(gs_res.res2d, \n",
    "             column=\"FDR q-val\",\n",
    "             title='KEGG_2021_Human',\n",
    "             cmap=plt.cm.viridis, \n",
    "             size=5, \n",
    "             figsize=(4,5), cutoff=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "9899f49d-cbe9-451f-bfa1-090798007bf8"
    }
   },
   "source": [
    "### Command line usage "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4462f89a-119f-4144-b796-b4b9451c9a44"
    }
   },
   "source": [
    "You may also want to use gsea in command line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !gseapy gsea -d ./data/P53_resampling_data.txt \\\n",
    "#              -g KEGG_2016 -c ./data/P53.cls \\\n",
    "#              -o test/gsea_reprot_2 \\\n",
    "#              -v --no-plot \\\n",
    "#              -t phenotype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single Sample GSEA example\n",
    "\n",
    "\n",
    "What's ssGSEA? Which one should I use? Prerank or ssGSEA\n",
    "\n",
    "see FAQ [here](https://github.com/zqfang/GSEApy/wiki/FAQ)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Input format  \n",
    "Assign \n",
    "- `data` with \n",
    "  - a txt file, gct file, \n",
    "  - pd.DataFrame\n",
    "  - pd.Seires(gene name as index)\n",
    "  \n",
    "- gene_sets with:\n",
    "\n",
    "```python\n",
    "    gene_sets=\"KEGG_2016\",\n",
    "    gene_sets=\"KEGG_2016,KEGG2013\",\n",
    "    gene_sets=\"./data/genes.gmt\",\n",
    "    gene_sets=[\"KEGG_2016\",\"./data/genes.gmt\"],\n",
    "    gene_sets={'A':['gene1', 'gene2',...],\n",
    "               'B':['gene2', 'gene4',...],\n",
    "               ...}\n",
    "```\n",
    "\n",
    "\n",
    "#### NOTE: UPCASES for gene symbols by Default\n",
    "\n",
    "1. Gene symbols are all \"UPCASES\" in the Enrichr Libaries. You should convert your input gene identifier to \"UPCASES\" first.\n",
    "2. If input `gmt`, `dict` object, please refer to `1.2 Mouse gene symbols maps to Human, or Vice Versa` (in this page) to convert gene identifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-25 10:46:59,844 [WARNING] Found duplicated gene names, values averaged by gene names!\n"
     ]
    }
   ],
   "source": [
    "import gseapy as gp\n",
    "# txt, gct file input\n",
    "ss = gp.ssgsea(data='./tests/extdata/Leukemia_hgu95av2.trim.txt',\n",
    "               gene_sets='./tests/extdata/h.all.v7.0.symbols.gmt',\n",
    "               outdir=None, \n",
    "               sample_norm_method='rank', # choose 'custom' will only use the raw value of `data`\n",
    "               no_plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Term</th>\n",
       "      <th>ES</th>\n",
       "      <th>NES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALL_2</td>\n",
       "      <td>HALLMARK_MYC_TARGETS_V1</td>\n",
       "      <td>3393.823575</td>\n",
       "      <td>0.707975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALL_12</td>\n",
       "      <td>HALLMARK_MYC_TARGETS_V1</td>\n",
       "      <td>3385.626111</td>\n",
       "      <td>0.706265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AML_11</td>\n",
       "      <td>HALLMARK_MYC_TARGETS_V1</td>\n",
       "      <td>3359.186716</td>\n",
       "      <td>0.700749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALL_14</td>\n",
       "      <td>HALLMARK_MYC_TARGETS_V1</td>\n",
       "      <td>3348.938881</td>\n",
       "      <td>0.698611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALL_17</td>\n",
       "      <td>HALLMARK_MYC_TARGETS_V1</td>\n",
       "      <td>3335.065348</td>\n",
       "      <td>0.695717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name                     Term           ES       NES\n",
       "0   ALL_2  HALLMARK_MYC_TARGETS_V1  3393.823575  0.707975\n",
       "1  ALL_12  HALLMARK_MYC_TARGETS_V1  3385.626111  0.706265\n",
       "2  AML_11  HALLMARK_MYC_TARGETS_V1  3359.186716  0.700749\n",
       "3  ALL_14  HALLMARK_MYC_TARGETS_V1  3348.938881  0.698611\n",
       "4  ALL_17  HALLMARK_MYC_TARGETS_V1  3335.065348  0.695717"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.res2d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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       "      <th>ATXN1</th>\n",
       "      <td>16.456753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN4</th>\n",
       "      <td>13.989493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALM1</th>\n",
       "      <td>13.745533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DLG4</th>\n",
       "      <td>12.796588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRE11A</th>\n",
       "      <td>12.787631</td>\n",
       "    </tr>\n",
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       "                1\n",
       "0                \n",
       "ATXN1   16.456753\n",
       "UBQLN4  13.989493\n",
       "CALM1   13.745533\n",
       "DLG4    12.796588\n",
       "MRE11A  12.787631"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or assign a dataframe, or Series to ssgsea()\n",
    "ssdf = pd.read_csv(\"./tests/data/temp.rnk\", header=None,index_col=0,  sep=\"\\t\")\n",
    "ssdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataframe with one column is also supported by ssGSEA or Prerank\n",
    "# But you have to set gene_names as index\n",
    "ssdf2 = ssdf.squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Series, DataFrame Example\n",
    "# supports dataframe and series\n",
    "temp  = gp.ssgsea(data=ssdf2, gene_sets=\"./tests/data/temp.gmt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Access Enrichment Score (ES) and NES\n",
    "\n",
    "Results are saved to obj.res2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Term</th>\n",
       "      <th>ES</th>\n",
       "      <th>NES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>ALL_1</td>\n",
       "      <td>HALLMARK_PANCREAS_BETA_CELLS</td>\n",
       "      <td>-1280.654659</td>\n",
       "      <td>-0.267153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>ALL_1</td>\n",
       "      <td>HALLMARK_APOPTOSIS</td>\n",
       "      <td>970.818772</td>\n",
       "      <td>0.202519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1774</th>\n",
       "      <td>ALL_1</td>\n",
       "      <td>HALLMARK_HEDGEHOG_SIGNALING</td>\n",
       "      <td>431.446694</td>\n",
       "      <td>0.090003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>ALL_1</td>\n",
       "      <td>HALLMARK_INTERFERON_ALPHA_RESPONSE</td>\n",
       "      <td>1721.458034</td>\n",
       "      <td>0.359108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1778</th>\n",
       "      <td>ALL_1</td>\n",
       "      <td>HALLMARK_BILE_ACID_METABOLISM</td>\n",
       "      <td>-429.127871</td>\n",
       "      <td>-0.089519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name                                Term           ES       NES\n",
       "601   ALL_1        HALLMARK_PANCREAS_BETA_CELLS -1280.654659 -0.267153\n",
       "934   ALL_1                  HALLMARK_APOPTOSIS   970.818772  0.202519\n",
       "1774  ALL_1         HALLMARK_HEDGEHOG_SIGNALING   431.446694  0.090003\n",
       "279   ALL_1  HALLMARK_INTERFERON_ALPHA_RESPONSE  1721.458034  0.359108\n",
       "1778  ALL_1       HALLMARK_BILE_ACID_METABOLISM  -429.127871 -0.089519"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NES and ES\n",
    "ss.res2d.sort_values('Name').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Name</th>\n",
       "      <th>ALL_1</th>\n",
       "      <th>ALL_10</th>\n",
       "      <th>ALL_11</th>\n",
       "      <th>ALL_12</th>\n",
       "      <th>ALL_13</th>\n",
       "      <th>ALL_14</th>\n",
       "      <th>ALL_15</th>\n",
       "      <th>ALL_16</th>\n",
       "      <th>ALL_17</th>\n",
       "      <th>ALL_18</th>\n",
       "      <th>...</th>\n",
       "      <th>AML_22</th>\n",
       "      <th>AML_23</th>\n",
       "      <th>AML_24</th>\n",
       "      <th>AML_3</th>\n",
       "      <th>AML_4</th>\n",
       "      <th>AML_5</th>\n",
       "      <th>AML_6</th>\n",
       "      <th>AML_7</th>\n",
       "      <th>AML_8</th>\n",
       "      <th>AML_9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Term</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ADIPOGENESIS</th>\n",
       "      <td>0.287384</td>\n",
       "      <td>0.274548</td>\n",
       "      <td>0.290059</td>\n",
       "      <td>0.285388</td>\n",
       "      <td>0.322757</td>\n",
       "      <td>0.305239</td>\n",
       "      <td>0.275686</td>\n",
       "      <td>0.266209</td>\n",
       "      <td>0.315803</td>\n",
       "      <td>0.282617</td>\n",
       "      <td>...</td>\n",
       "      <td>0.277755</td>\n",
       "      <td>0.261477</td>\n",
       "      <td>0.200083</td>\n",
       "      <td>0.312948</td>\n",
       "      <td>0.342963</td>\n",
       "      <td>0.253282</td>\n",
       "      <td>0.298924</td>\n",
       "      <td>0.410395</td>\n",
       "      <td>0.387433</td>\n",
       "      <td>0.343606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ALLOGRAFT_REJECTION</th>\n",
       "      <td>0.06177</td>\n",
       "      <td>0.028062</td>\n",
       "      <td>0.096589</td>\n",
       "      <td>0.080713</td>\n",
       "      <td>0.082701</td>\n",
       "      <td>0.102735</td>\n",
       "      <td>0.12525</td>\n",
       "      <td>0.147262</td>\n",
       "      <td>0.124621</td>\n",
       "      <td>0.091077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.185738</td>\n",
       "      <td>0.157852</td>\n",
       "      <td>0.055585</td>\n",
       "      <td>0.218827</td>\n",
       "      <td>0.172395</td>\n",
       "      <td>0.199077</td>\n",
       "      <td>0.158945</td>\n",
       "      <td>0.13835</td>\n",
       "      <td>0.110787</td>\n",
       "      <td>0.121643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ANDROGEN_RESPONSE</th>\n",
       "      <td>0.133453</td>\n",
       "      <td>0.113911</td>\n",
       "      <td>0.193074</td>\n",
       "      <td>0.201531</td>\n",
       "      <td>0.151001</td>\n",
       "      <td>0.12967</td>\n",
       "      <td>0.173563</td>\n",
       "      <td>0.144836</td>\n",
       "      <td>0.180214</td>\n",
       "      <td>0.180801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.180443</td>\n",
       "      <td>0.188891</td>\n",
       "      <td>0.197979</td>\n",
       "      <td>0.174892</td>\n",
       "      <td>0.14285</td>\n",
       "      <td>0.184843</td>\n",
       "      <td>0.157449</td>\n",
       "      <td>0.162843</td>\n",
       "      <td>0.180475</td>\n",
       "      <td>0.181878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ANGIOGENESIS</th>\n",
       "      <td>-0.113481</td>\n",
       "      <td>-0.182411</td>\n",
       "      <td>-0.195637</td>\n",
       "      <td>-0.094817</td>\n",
       "      <td>-0.163717</td>\n",
       "      <td>-0.139243</td>\n",
       "      <td>-0.119084</td>\n",
       "      <td>-0.154526</td>\n",
       "      <td>-0.06829</td>\n",
       "      <td>-0.121156</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054883</td>\n",
       "      <td>-0.023782</td>\n",
       "      <td>0.119022</td>\n",
       "      <td>-0.067741</td>\n",
       "      <td>0.04843</td>\n",
       "      <td>0.012808</td>\n",
       "      <td>0.032505</td>\n",
       "      <td>-0.024058</td>\n",
       "      <td>-0.039492</td>\n",
       "      <td>-0.043769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_APICAL_JUNCTION</th>\n",
       "      <td>0.051372</td>\n",
       "      <td>0.063763</td>\n",
       "      <td>0.054601</td>\n",
       "      <td>0.014385</td>\n",
       "      <td>0.049019</td>\n",
       "      <td>0.05269</td>\n",
       "      <td>0.064787</td>\n",
       "      <td>0.052192</td>\n",
       "      <td>0.05607</td>\n",
       "      <td>0.064936</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10927</td>\n",
       "      <td>0.090065</td>\n",
       "      <td>0.155801</td>\n",
       "      <td>0.091556</td>\n",
       "      <td>0.110045</td>\n",
       "      <td>0.101659</td>\n",
       "      <td>0.128808</td>\n",
       "      <td>0.095511</td>\n",
       "      <td>0.080076</td>\n",
       "      <td>0.098644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Name                             ALL_1    ALL_10    ALL_11    ALL_12  \\\n",
       "Term                                                                   \n",
       "HALLMARK_ADIPOGENESIS         0.287384  0.274548  0.290059  0.285388   \n",
       "HALLMARK_ALLOGRAFT_REJECTION   0.06177  0.028062  0.096589  0.080713   \n",
       "HALLMARK_ANDROGEN_RESPONSE    0.133453  0.113911  0.193074  0.201531   \n",
       "HALLMARK_ANGIOGENESIS        -0.113481 -0.182411 -0.195637 -0.094817   \n",
       "HALLMARK_APICAL_JUNCTION      0.051372  0.063763  0.054601  0.014385   \n",
       "\n",
       "Name                            ALL_13    ALL_14    ALL_15    ALL_16  \\\n",
       "Term                                                                   \n",
       "HALLMARK_ADIPOGENESIS         0.322757  0.305239  0.275686  0.266209   \n",
       "HALLMARK_ALLOGRAFT_REJECTION  0.082701  0.102735   0.12525  0.147262   \n",
       "HALLMARK_ANDROGEN_RESPONSE    0.151001   0.12967  0.173563  0.144836   \n",
       "HALLMARK_ANGIOGENESIS        -0.163717 -0.139243 -0.119084 -0.154526   \n",
       "HALLMARK_APICAL_JUNCTION      0.049019   0.05269  0.064787  0.052192   \n",
       "\n",
       "Name                            ALL_17    ALL_18  ...    AML_22    AML_23  \\\n",
       "Term                                              ...                       \n",
       "HALLMARK_ADIPOGENESIS         0.315803  0.282617  ...  0.277755  0.261477   \n",
       "HALLMARK_ALLOGRAFT_REJECTION  0.124621  0.091077  ...  0.185738  0.157852   \n",
       "HALLMARK_ANDROGEN_RESPONSE    0.180214  0.180801  ...  0.180443  0.188891   \n",
       "HALLMARK_ANGIOGENESIS         -0.06829 -0.121156  ...  0.054883 -0.023782   \n",
       "HALLMARK_APICAL_JUNCTION       0.05607  0.064936  ...   0.10927  0.090065   \n",
       "\n",
       "Name                            AML_24     AML_3     AML_4     AML_5  \\\n",
       "Term                                                                   \n",
       "HALLMARK_ADIPOGENESIS         0.200083  0.312948  0.342963  0.253282   \n",
       "HALLMARK_ALLOGRAFT_REJECTION  0.055585  0.218827  0.172395  0.199077   \n",
       "HALLMARK_ANDROGEN_RESPONSE    0.197979  0.174892   0.14285  0.184843   \n",
       "HALLMARK_ANGIOGENESIS         0.119022 -0.067741   0.04843  0.012808   \n",
       "HALLMARK_APICAL_JUNCTION      0.155801  0.091556  0.110045  0.101659   \n",
       "\n",
       "Name                             AML_6     AML_7     AML_8     AML_9  \n",
       "Term                                                                  \n",
       "HALLMARK_ADIPOGENESIS         0.298924  0.410395  0.387433  0.343606  \n",
       "HALLMARK_ALLOGRAFT_REJECTION  0.158945   0.13835  0.110787  0.121643  \n",
       "HALLMARK_ANDROGEN_RESPONSE    0.157449  0.162843  0.180475  0.181878  \n",
       "HALLMARK_ANGIOGENESIS         0.032505 -0.024058 -0.039492 -0.043769  \n",
       "HALLMARK_APICAL_JUNCTION      0.128808  0.095511  0.080076  0.098644  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nes = ss.res2d.pivot(index='Term', columns='Name', values='NES')\n",
    "nes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning !!!**\n",
    "\n",
    "if you set permutation_num > 0, ssgsea will become `prerank` with ssGSEA statistics. **DO NOT** use this, unless you known what you are doing !\n",
    "\n",
    "```python\n",
    "ss_permut = gp.ssgsea(data=\"./tests/extdata/Leukemia_hgu95av2.trim.txt\",\n",
    "               gene_sets=\"./tests/extdata/h.all.v7.0.symbols.gmt\", \n",
    "               outdir=None, \n",
    "               sample_norm_method='rank', # choose 'custom' for your custom metric\n",
    "               permutation_num=20, # set permutation_num > 0, it will act like prerank tool\n",
    "               no_plot=True, # skip plotting, because you don't need these figures\n",
    "               processes=4, seed=9)\n",
    "ss_permut.res2d.head(5)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Command line usage of ssGSEA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !gseapy ssgsea -d ./data/testSet_rand1200.gct \\\n",
    "#                -g data/temp.gmt \\\n",
    "#                -o test/ssgsea_report2  \\\n",
    "#                -p 4 --no-plot "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GSVA example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-25 10:47:01,160 [WARNING] Found duplicated gene names, values averaged by gene names!\n"
     ]
    }
   ],
   "source": [
    "import gseapy as gp\n",
    "# txt, gct file input\n",
    "es = gp.gsva(data='./tests/extdata/Leukemia_hgu95av2.trim.txt',\n",
    "             gene_sets='./tests/extdata/h.all.v7.0.symbols.gmt',\n",
    "             outdir=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Name</th>\n",
       "      <th>ALL_1</th>\n",
       "      <th>ALL_10</th>\n",
       "      <th>ALL_11</th>\n",
       "      <th>ALL_12</th>\n",
       "      <th>ALL_13</th>\n",
       "      <th>ALL_14</th>\n",
       "      <th>ALL_15</th>\n",
       "      <th>ALL_16</th>\n",
       "      <th>ALL_17</th>\n",
       "      <th>ALL_18</th>\n",
       "      <th>...</th>\n",
       "      <th>AML_22</th>\n",
       "      <th>AML_23</th>\n",
       "      <th>AML_24</th>\n",
       "      <th>AML_3</th>\n",
       "      <th>AML_4</th>\n",
       "      <th>AML_5</th>\n",
       "      <th>AML_6</th>\n",
       "      <th>AML_7</th>\n",
       "      <th>AML_8</th>\n",
       "      <th>AML_9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Term</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ADIPOGENESIS</th>\n",
       "      <td>-0.21331</td>\n",
       "      <td>-0.08096</td>\n",
       "      <td>0.003289</td>\n",
       "      <td>-0.017909</td>\n",
       "      <td>0.207841</td>\n",
       "      <td>0.023294</td>\n",
       "      <td>-0.085392</td>\n",
       "      <td>-0.221273</td>\n",
       "      <td>0.16147</td>\n",
       "      <td>-0.01825</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03344</td>\n",
       "      <td>-0.190436</td>\n",
       "      <td>-0.0985</td>\n",
       "      <td>0.105208</td>\n",
       "      <td>0.196799</td>\n",
       "      <td>-0.296305</td>\n",
       "      <td>-0.084042</td>\n",
       "      <td>0.450832</td>\n",
       "      <td>0.226921</td>\n",
       "      <td>0.209835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ALLOGRAFT_REJECTION</th>\n",
       "      <td>-0.210468</td>\n",
       "      <td>-0.373787</td>\n",
       "      <td>-0.086016</td>\n",
       "      <td>-0.169623</td>\n",
       "      <td>-0.158775</td>\n",
       "      <td>-0.016488</td>\n",
       "      <td>-0.050703</td>\n",
       "      <td>0.10443</td>\n",
       "      <td>-0.075816</td>\n",
       "      <td>-0.193654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023653</td>\n",
       "      <td>0.032892</td>\n",
       "      <td>-0.113577</td>\n",
       "      <td>0.30703</td>\n",
       "      <td>0.134581</td>\n",
       "      <td>0.188905</td>\n",
       "      <td>0.132169</td>\n",
       "      <td>0.024078</td>\n",
       "      <td>-0.092054</td>\n",
       "      <td>-0.195987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ANDROGEN_RESPONSE</th>\n",
       "      <td>-0.13633</td>\n",
       "      <td>-0.308572</td>\n",
       "      <td>0.008126</td>\n",
       "      <td>0.04849</td>\n",
       "      <td>-0.061181</td>\n",
       "      <td>-0.203036</td>\n",
       "      <td>0.070416</td>\n",
       "      <td>-0.12524</td>\n",
       "      <td>0.080075</td>\n",
       "      <td>0.022248</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031898</td>\n",
       "      <td>0.064394</td>\n",
       "      <td>0.070232</td>\n",
       "      <td>0.199349</td>\n",
       "      <td>-0.079399</td>\n",
       "      <td>-0.016658</td>\n",
       "      <td>-0.127327</td>\n",
       "      <td>0.018847</td>\n",
       "      <td>0.121426</td>\n",
       "      <td>0.163149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_ANGIOGENESIS</th>\n",
       "      <td>0.035895</td>\n",
       "      <td>-0.287645</td>\n",
       "      <td>-0.214951</td>\n",
       "      <td>-0.291145</td>\n",
       "      <td>-0.311917</td>\n",
       "      <td>-0.236717</td>\n",
       "      <td>-0.345662</td>\n",
       "      <td>-0.250202</td>\n",
       "      <td>-0.233296</td>\n",
       "      <td>-0.318353</td>\n",
       "      <td>...</td>\n",
       "      <td>0.244374</td>\n",
       "      <td>-0.076852</td>\n",
       "      <td>-0.010928</td>\n",
       "      <td>-0.210787</td>\n",
       "      <td>0.387912</td>\n",
       "      <td>0.269447</td>\n",
       "      <td>0.34823</td>\n",
       "      <td>0.157249</td>\n",
       "      <td>0.075479</td>\n",
       "      <td>-0.064515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALLMARK_APICAL_JUNCTION</th>\n",
       "      <td>-0.088652</td>\n",
       "      <td>-0.128757</td>\n",
       "      <td>-0.050282</td>\n",
       "      <td>-0.248682</td>\n",
       "      <td>-0.145164</td>\n",
       "      <td>0.001997</td>\n",
       "      <td>-0.082962</td>\n",
       "      <td>-0.091691</td>\n",
       "      <td>-0.168941</td>\n",
       "      <td>-0.139766</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>-0.067385</td>\n",
       "      <td>0.062719</td>\n",
       "      <td>-0.022434</td>\n",
       "      <td>0.076593</td>\n",
       "      <td>0.138664</td>\n",
       "      <td>0.240647</td>\n",
       "      <td>0.039307</td>\n",
       "      <td>0.016764</td>\n",
       "      <td>0.057512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Name                             ALL_1    ALL_10    ALL_11    ALL_12  \\\n",
       "Term                                                                   \n",
       "HALLMARK_ADIPOGENESIS         -0.21331  -0.08096  0.003289 -0.017909   \n",
       "HALLMARK_ALLOGRAFT_REJECTION -0.210468 -0.373787 -0.086016 -0.169623   \n",
       "HALLMARK_ANDROGEN_RESPONSE    -0.13633 -0.308572  0.008126   0.04849   \n",
       "HALLMARK_ANGIOGENESIS         0.035895 -0.287645 -0.214951 -0.291145   \n",
       "HALLMARK_APICAL_JUNCTION     -0.088652 -0.128757 -0.050282 -0.248682   \n",
       "\n",
       "Name                            ALL_13    ALL_14    ALL_15    ALL_16  \\\n",
       "Term                                                                   \n",
       "HALLMARK_ADIPOGENESIS         0.207841  0.023294 -0.085392 -0.221273   \n",
       "HALLMARK_ALLOGRAFT_REJECTION -0.158775 -0.016488 -0.050703   0.10443   \n",
       "HALLMARK_ANDROGEN_RESPONSE   -0.061181 -0.203036  0.070416  -0.12524   \n",
       "HALLMARK_ANGIOGENESIS        -0.311917 -0.236717 -0.345662 -0.250202   \n",
       "HALLMARK_APICAL_JUNCTION     -0.145164  0.001997 -0.082962 -0.091691   \n",
       "\n",
       "Name                            ALL_17    ALL_18  ...    AML_22    AML_23  \\\n",
       "Term                                              ...                       \n",
       "HALLMARK_ADIPOGENESIS          0.16147  -0.01825  ...   0.03344 -0.190436   \n",
       "HALLMARK_ALLOGRAFT_REJECTION -0.075816 -0.193654  ...  0.023653  0.032892   \n",
       "HALLMARK_ANDROGEN_RESPONSE    0.080075  0.022248  ...  0.031898  0.064394   \n",
       "HALLMARK_ANGIOGENESIS        -0.233296 -0.318353  ...  0.244374 -0.076852   \n",
       "HALLMARK_APICAL_JUNCTION     -0.168941 -0.139766  ...  0.005859 -0.067385   \n",
       "\n",
       "Name                            AML_24     AML_3     AML_4     AML_5  \\\n",
       "Term                                                                   \n",
       "HALLMARK_ADIPOGENESIS          -0.0985  0.105208  0.196799 -0.296305   \n",
       "HALLMARK_ALLOGRAFT_REJECTION -0.113577   0.30703  0.134581  0.188905   \n",
       "HALLMARK_ANDROGEN_RESPONSE    0.070232  0.199349 -0.079399 -0.016658   \n",
       "HALLMARK_ANGIOGENESIS        -0.010928 -0.210787  0.387912  0.269447   \n",
       "HALLMARK_APICAL_JUNCTION      0.062719 -0.022434  0.076593  0.138664   \n",
       "\n",
       "Name                             AML_6     AML_7     AML_8     AML_9  \n",
       "Term                                                                  \n",
       "HALLMARK_ADIPOGENESIS        -0.084042  0.450832  0.226921  0.209835  \n",
       "HALLMARK_ALLOGRAFT_REJECTION  0.132169  0.024078 -0.092054 -0.195987  \n",
       "HALLMARK_ANDROGEN_RESPONSE   -0.127327  0.018847  0.121426  0.163149  \n",
       "HALLMARK_ANGIOGENESIS          0.34823  0.157249  0.075479 -0.064515  \n",
       "HALLMARK_APICAL_JUNCTION      0.240647  0.039307  0.016764  0.057512  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es.res2d.pivot(index='Term', columns='Name', values='ES').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Command line usage of GSVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !gseapy ssgsea -d ./tests/data/expr.gsva.csv \\\n",
    "#                -g ./tests/data/geneset.gsva.gmt \\\n",
    "#                -o test/gsva_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replot Example\n",
    "\n",
    "### Locate your directory\n",
    "\n",
    "Notes: ``replot`` module need to find edb folder to work properly.\n",
    "keep the file tree like this:\n",
    "```\n",
    "data\n",
    " |--- edb\n",
    " |    |--- C1OE.cls\n",
    " |    |--- gene_sets.gmt\n",
    " |    |--- gsea_data.gsea_data.rnk\n",
    " |    |--- results.edb\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run command inside python console\n",
    "rep = gp.replot(indir=\"./tests/data\", outdir=\"test/replot_test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Command line usage of replot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !gseapy replot -i data -o test/replot_test"
   ]
  }
 ],
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